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app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 from transformers import pipeline
-import os
+import json
 
 # Load model
 print("Loading model...")
@@ -12,7 +12,7 @@ classifier = pipeline(
 print("Model loaded!")
 
 def classify_ethics(text: str, guideline_id: str = "", guideline_name: str = ""):
-    """Classify text for ethics guideline compliance."""
+    """Classify single text for ethics guideline compliance."""
     
     # Format input like training data
     if guideline_id and guideline_name:
@@ -39,23 +39,102 @@ def classify_ethics(text: str, guideline_id: str = "", guideline_name: str = "")
         "input_preview": input_text[:100] + "..."
     }
 
-# Create Gradio interface
-demo = gr.Interface(
-    fn=classify_ethics,
-    inputs=[
-        gr.Textbox(label="Text to Analyze", lines=5, placeholder="Enter the text from research proposal..."),
-        gr.Textbox(label="Guideline ID (optional)", placeholder="e.g., 1.1"),
-        gr.Textbox(label="Guideline Name (optional)", placeholder="e.g., Objectives"),
-    ],
-    outputs=gr.JSON(label="Classification Result"),
-    title="Ethics Review Classifier",
-    description="Classify research proposal text against ethics guidelines. Returns ADDRESSED or NEEDS_REVISION.",
-    examples=[
-        ["The general objective is to develop an AI ethics review system. Specific objectives: 1) Create scanning module 2) Implement matching.", "1.1", "Objectives"],
-        ["All participant data will be encrypted using AES-256 and stored securely.", "3.2", "Privacy and confidentiality"],
-        ["The study explores innovative approaches.", "1.7", "Sampling design and size"],
-    ],
-)
+
+def classify_batch(batch_json: str):
+    """
+    Classify multiple texts in a single API call for better performance.
+    
+    Input: JSON string with format:
+    [
+        {"text": "...", "guideline_id": "1.1", "guideline_name": "Objectives"},
+        {"text": "...", "guideline_id": "3.2", "guideline_name": "Privacy"},
+        ...
+    ]
+    
+    Output: JSON string with results for each input.
+    """
+    try:
+        items = json.loads(batch_json)
+    except json.JSONDecodeError as e:
+        return json.dumps({"error": f"Invalid JSON: {str(e)}"})
+    
+    if not isinstance(items, list):
+        return json.dumps({"error": "Input must be a JSON array"})
+    
+    if len(items) > 50:
+        return json.dumps({"error": "Maximum 50 items per batch"})
+    
+    # Prepare all inputs
+    formatted_inputs = []
+    for item in items:
+        text = item.get("text", "")
+        g_id = item.get("guideline_id", "")
+        g_name = item.get("guideline_name", "")
+        
+        if g_id and g_name:
+            input_text = f"Guideline {g_id} {g_name}: {text}"
+        else:
+            input_text = text
+        
+        formatted_inputs.append(input_text[:1500])
+    
+    # Run batch inference (much faster than individual calls)
+    predictions = classifier(formatted_inputs)
+    
+    # Format results
+    results = []
+    for pred in predictions:
+        label = pred['label']
+        if label == "LABEL_0":
+            label = "ADDRESSED"
+        elif label == "LABEL_1":
+            label = "NEEDS_REVISION"
+        
+        results.append({
+            "label": label,
+            "score": round(pred['score'], 4)
+        })
+    
+    return json.dumps(results)
+
+
+# Create Gradio interface with both single and batch endpoints
+with gr.Blocks(title="Ethics Review Classifier") as demo:
+    gr.Markdown("# Ethics Review Classifier")
+    gr.Markdown("Classify research proposal text against ethics guidelines. Returns ADDRESSED or NEEDS_REVISION.")
+    
+    with gr.Tab("Single Classification"):
+        with gr.Row():
+            with gr.Column():
+                text_input = gr.Textbox(label="Text to Analyze", lines=5, placeholder="Enter the text from research proposal...")
+                id_input = gr.Textbox(label="Guideline ID (optional)", placeholder="e.g., 1.1")
+                name_input = gr.Textbox(label="Guideline Name (optional)", placeholder="e.g., Objectives")
+                single_btn = gr.Button("Classify", variant="primary")
+            with gr.Column():
+                single_output = gr.JSON(label="Result")
+        
+        single_btn.click(classify_ethics, inputs=[text_input, id_input, name_input], outputs=single_output)
+        
+        gr.Examples(
+            examples=[
+                ["The general objective is to develop an AI ethics review system. Specific objectives: 1) Create scanning module 2) Implement matching.", "1.1", "Objectives"],
+                ["All participant data will be encrypted using AES-256 and stored securely.", "3.2", "Privacy and confidentiality"],
+                ["The study explores innovative approaches.", "1.7", "Sampling design and size"],
+            ],
+            inputs=[text_input, id_input, name_input],
+        )
+    
+    with gr.Tab("Batch Classification (Fast)"):
+        gr.Markdown("**For API users:** Send up to 50 items in one request for faster processing.")
+        batch_input = gr.Textbox(
+            label="Batch Input (JSON Array)", 
+            lines=10, 
+            placeholder='[{"text": "...", "guideline_id": "1.1", "guideline_name": "Objectives"}, ...]'
+        )
+        batch_btn = gr.Button("Classify Batch", variant="primary")
+        batch_output = gr.Textbox(label="Batch Results (JSON)", lines=10)
+        
+        batch_btn.click(classify_batch, inputs=[batch_input], outputs=batch_output)
 
 # Launch with API enabled
 demo.launch()